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Consistent Estimation of the “True” Fixed-effects Stochastic Frontier Model

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Abstract

The classic stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a,b) proposed a fixed-effects model specification that distinguishes these two latent components and allows a time varying inefficiency distribution. However, the maximum likelihood estimator proposed by Greene leads to biased inefficiency estimates due to the incidental parameters problem. In this paper, we propose two alternative estimation procedures that, by relying on a first difference data transformation, achieve consistency for n goes to infinity with fixed T. Evidence from Monte Carlo simulations shows good finite sample performances of both approaches even in presence of small samples.

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 231.

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Length: 29 pages
Date of creation: 18 Apr 2012
Date of revision: 18 Apr 2012
Handle: RePEc:rtv:ceisrp:231

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Web: http://www.ceistorvergata.it

Related research

Keywords: Stochastic frontiers; Fixed-effects; Panel data; Marginal simulated likelihood; Pairwise differencing;

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References

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  1. Poirier, Dale J & Ruud, Paul A, 1988. "Probit with Dependent Observations," Review of Economic Studies, Wiley Blackwell, vol. 55(4), pages 593-614, October.
  2. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  3. Honore, Bo E. & Powell, James L., 1994. "Pairwise difference estimators of censored and truncated regression models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 241-278.
  4. Abrevaya, Jason, 1999. "Leapfrog estimation of a fixed-effects model with unknown transformation of the dependent variable," Journal of Econometrics, Elsevier, vol. 93(2), pages 203-228, December.
  5. Gian Paolo Barbetta & Gilberto Turati & Angelo Zago, 2004. "Behavioral Differences Between Public and Private Not-For-Profit Hospitals in the Italian National Health Service," Working Papers 12, University of Verona, Department of Economics.
  6. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
  7. Carlos Martins-Filho & Feng Yao, 2010. "A note on some properties of a skew-normal density," Working Papers 10-10, Department of Economics, West Virginia University.
  8. Silvio Daidone & Francesco D’Amico, 2009. "Technical efficiency, specialization and ownership form: evidences from a pooling of Italian hospitals," Journal of Productivity Analysis, Springer, vol. 32(3), pages 203-216, December.
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  10. Wang, Wei Siang & Schmidt, Peter, 2009. "On the distribution of estimated technical efficiency in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 148(1), pages 36-45, January.
  11. Andres Aradillas-Lopez & Bo E. Honoré & James L. Powell, 2007. "Pairwise Difference Estimation With Nonparametric Control Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1119-1158, November.
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  13. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
  14. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
  15. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
  16. Wang, Hung-Jen, 2006. "Stochastic frontier models," MPRA Paper 31079, University Library of Munich, Germany.
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Citations

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Cited by:
  1. Ricardo Fenochietto & Carola Pessino, 2013. "Understanding Countries’ Tax Effort," IMF Working Papers 13/244, International Monetary Fund.
  2. Ferro, Gustavo & Lentini, Emilio J. & Mercadier, Augusto C. & Romero, Carlos A., 2014. "Efficiency in Brazil's water and sanitation sector and its relationship with regional provision, property and the independence of operators," Utilities Policy, Elsevier, vol. 28(C), pages 42-51.
  3. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
  4. Ipatova, Irina & Peresetsky, Аnatoly, 2013. "Technical efficiency of Russian plastic and rubber production firms," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 32(4), pages 71-92.

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